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dc.contributor.author | Trull, Óscar | es_ES |
dc.contributor.author | García-Díaz, J. Carlos | es_ES |
dc.contributor.author | Troncoso, Alicia | es_ES |
dc.date.accessioned | 2020-12-01T04:32:33Z | |
dc.date.available | 2020-12-01T04:32:33Z | |
dc.date.issued | 2019-03-02 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/156110 | |
dc.description.abstract | [EN] Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt-Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt-Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt-Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods. | es_ES |
dc.description.sponsorship | The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Energies | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Time series | es_ES |
dc.subject | Forecasting | es_ES |
dc.subject | Exponential smoothing | es_ES |
dc.subject | Electricity demand | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/en12061083 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88209-C2-1-R/ES/BIG DATA STREAMING: ANALISIS DE DATOS MASIVOS CONTINUOS. MODELOS PREDICTIVOS/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
dc.description.bibliographicCitation | Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies. 12(6):1-16. https://doi.org/10.3390/en12061083 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/en12061083 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 16 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 12 | es_ES |
dc.description.issue | 6 | es_ES |
dc.identifier.eissn | 1996-1073 | es_ES |
dc.relation.pasarela | S\386252 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
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